Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?

Giordano Valente; Lorenzo Pitto; Debora Testi; Ajay Seth; Scott L Delp; Rita Stagni; Marco Viceconti; Fulvia Taddei

doi:10.1371/journal.pone.0112625

Are subject-specific musculoskeletal models robust to the uncertainties in parameter identification?

PLoS One. 2014 Nov 12;9(11):e112625. doi: 10.1371/journal.pone.0112625. eCollection 2014.

Authors

Giordano Valente¹, Lorenzo Pitto¹, Debora Testi², Ajay Seth³, Scott L Delp⁴, Rita Stagni⁵, Marco Viceconti⁶, Fulvia Taddei¹

Affiliations

¹ Medical Technology Laboratory, Rizzoli Orthopaedic Institute, Bologna, Italy.
² BioComputing Competence Centre, SCS s.r.l., Bologna, Italy.
³ Department of Bioengineering, Stanford University, Stanford, California, United States of America.
⁴ Department of Bioengineering, Stanford University, Stanford, California, United States of America; Department of Mechanical Engineering, Stanford University, Stanford, California, United States of America.
⁵ Department of Electrical, Electronic and Information Engineering, University of Bologna, Bologna, Italy.
⁶ Department of Mechanical Engineering and INSIGNEO Institute for In Silico Medicine, University of Sheffield, Sheffield, United Kingdom.

Abstract

Subject-specific musculoskeletal modeling can be applied to study musculoskeletal disorders, allowing inclusion of personalized anatomy and properties. Independent of the tools used for model creation, there are unavoidable uncertainties associated with parameter identification, whose effect on model predictions is still not fully understood. The aim of the present study was to analyze the sensitivity of subject-specific model predictions (i.e., joint angles, joint moments, muscle and joint contact forces) during walking to the uncertainties in the identification of body landmark positions, maximum muscle tension and musculotendon geometry. To this aim, we created an MRI-based musculoskeletal model of the lower limbs, defined as a 7-segment, 10-degree-of-freedom articulated linkage, actuated by 84 musculotendon units. We then performed a Monte-Carlo probabilistic analysis perturbing model parameters according to their uncertainty, and solving a typical inverse dynamics and static optimization problem using 500 models that included the different sets of perturbed variable values. Model creation and gait simulations were performed by using freely available software that we developed to standardize the process of model creation, integrate with OpenSim and create probabilistic simulations of movement. The uncertainties in input variables had a moderate effect on model predictions, as muscle and joint contact forces showed maximum standard deviation of 0.3 times body-weight and maximum range of 2.1 times body-weight. In addition, the output variables significantly correlated with few input variables (up to 7 out of 312) across the gait cycle, including the geometry definition of larger muscles and the maximum muscle tension in limited gait portions. Although we found subject-specific models not markedly sensitive to parameter identification, researchers should be aware of the model precision in relation to the intended application. In fact, force predictions could be affected by an uncertainty in the same order of magnitude of its value, although this condition has low probability to occur.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adult
Biomechanical Phenomena / physiology
Computer Simulation
Gait / physiology*
Humans
Joints / physiology*
Knee Joint / physiology
Magnetic Resonance Imaging
Male
Models, Biological*
Muscle, Skeletal / physiology*
Walking / physiology*

Grants and funding

This study was supported by the EU-funded NMS Physiome project (FP7-ICT-248189), and supported in part by the EU-funded VPHOP project (FP7-ICT-223865). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Co-author Debora Testi is employed by BioComputing Competence Centre, SCS s.r.l. BioComputing Competence Centre, SCS s.r.l. provided support in the form of salary for author DT, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.